Tag Archives: Strategic Transformation

Mar

This article isn’t just about the application of analytics in healthcare. This is more about how analytics is being harnessed to evaluate the latest innovations in healthcare technology in order to help leaders in healthcare make policy decisions about embracing the new technology.

Techno-medical innovations

Over the last couple of decades, there have been quite a few noteworthy technological advancements in healthcare industry. Electronic health records (EHRs), HAART for HIV combined drug therapy, minimally invasive surgery, needle-free injection technology, MRI, genomics, and non-invasive diagnostics are just to name a few. These innovations are extraordinary because they are transforming the way patients are being diagnosed and treated in a better, faster, and safer way.

One such technological advancement was endoscopic surgery or minimally invasive surgery. This innovation revolutionized the way surgeries are performed. Knowledge@Wharton once ranked it tenth among the “Top 30 Innovations of the Last 30 Years” list. The conventional surgical procedures were highly invasive, riskier, painful, and time-consuming. They required long post-operative hospital stays and longer recovery times. Thanks to technological innovations, today, patients have an option of choosing either robotic surgery or endoscopic (non-robotic) surgery, which result in much shorter recovery times, less pain and dramatically reduced scarring. This augurs well for patients who are looking to return back to work quickly.

Minimally Invasive Cardiac Surgery

Many hospitals and cardiac care centers worldwide are evaluating the efficacy of the newer – minimal invasive approach – for specific cardiac surgical procedures. The conventional cardiac valve repair/replacement surgery involved opening up a patient with an 18 -20 cm vertical incision at the sternum. The newer minimally invasive procedure involves approaching the heart through a much smaller (horizontal up to 7cm or a key-hole) incision under the right breast. The new cardiac procedures are more complex for surgeons to perform as the area of access (to heart) narrows down drastically compared to the wider access that the conventional surgery allows. Nevertheless, the new procedure is believed to involve less bleeding, lower risk of infection, faster recovery times, and lesser expenses for patients.

Analytics in Healthcare – A Case Study

Our client# had been studying the effectiveness of the new method of cardiac surgery compared to the conventional way of performing the same procedure. The study allowed them to enroll patients for one of the two types of procedures depending upon a number of physiological and health factors of each patient. They performed the study over a period of three years and recorded the observations. The observed data included the type of procedure performed along with a number of associated output parameters such as hospital stay duration and pain levels experienced, among many others. A power of 80% was chosen; the data was collated and randomized for detailed analysis. Our analysts collaborated closely with the client to understand the nature and significance of each output parameter. We identified the right statistical tools and techniques to be applied based on the nature and type of the data to be analysed. The analyses were performed with a statistical significance level of 5%. The results were examined in detail both for statistical and clinical significance. The statistical test results were cross-checked quantitatively as well as qualitatively with subject matter experts for completeness and correctness in order to arrive at unambiguous conclusions. Each conclusion shared with the client was solidly backed with data. The results would help them make a fact-based policy decision to embrace the newer procedure for their center. Going beyond the statistical analysis, a predictive model was developed based on the results of the initial study. The predictive model would assist the client in determining the right approach to adopt for a future patient depending upon a number of factors. This is expected to improve the cardiac procedural outcome at the healthcare center.

Application of analytics in healthcare

Predictive modeling using machine learning is a powerful technique that helps in forecasting a probable outcome based on empirical data. Predictive modeling and analytics has tremendous potential in healthcare to improve the overall quality of patient care services. Analytics has shown promise to all the constituents involved in the healthcare sector viz. patients, physicians/surgeons, hospitals, pharmaceutical companies, insurance companies, and public health professionals.

Patients – be more aware of self-health

Some of the uses of predictive analytics include increased accuracy of diagnosis, early detection of a disease condition in at-risk patients using genomics, and evidence based medicine. In general, with the proliferation of wearables, patients can be more aware and assured of their own physical conditions.

Physicians/Surgeons – increase diagnostic accuracy

When a patient is visiting a physician complaining chest pain, it is often difficult for the physician to know whether the person needs hospitalization. If the doctor is using a well-tested predictive diagnostic system, in which he can accurately input the patient’s physical and clinical condition, then the system can assist the physician make an informed judgement.

On the treatment side, a physician can follow a patient’s data (or EHRs) for many years and can prescribe a treatment regime tailored to the patient’s specific condition. This fact-based treatment reduces the probability of causing any major side effects.

Hospitals – improve patient care with low mortality rates

Like the case study narrated above, predictive analytics can help hospitals and research centers in evaluating the efficacy of various procedures and treatments in order to improve the mortality and morbidity rates during the post-op period.

Researching a new drug and conducting a clinical trial for the new drug are two very lengthy, costly and resource intensive processes for pharma companies. The R&D process for pharma companies can become more productive by leveraging the power of machine learning to systematically test the mixtures of existing proven molecular components. This may help in identifying new drugs with higher probability for success. Moreover, predictive modeling can be implemented to test the effectiveness of new drugs in a faster and less expensive manner. This will not only help them bring the drug to the market more quickly, but will also reduce the overall healthcare costs per patient significantly.

Insurance Companies – reduce cost of insurance

Healthcare insurance service providers can implement predictive analytics models to better forecast insurance cost for individuals. Presently, the insurance cost is more a function of a person’s age, current medical condition, and the ‘plan’ they are opting for. Now, advancements in medical technology have made it possible to make genetic information and other healthcare related data easily available. Insurance providers can make use of this information to arrive at future medical expenses for a person, and make more informed decisions about the insurance costs associated with that individual. This will be a more realistic assessment of insurance needs for a person and will be beneficial to both sides in terms of provisions to be made.

Public Health (Professionals)

The World Health Organization defines public health as all privately and publicly sponsored measures to prevent disease, promote health, and prolong life among the population as a whole. Its activities aim to provide conditions in which people can be healthy and focus on entire populations, not on individual patients or diseases. Here, analytics can be implemented in predicting early detection of pandemics and flu outbreaks. GoogleFlu was a project which estimated Flu and Dengue fever based on search patterns. While the project is not publishing anymore, empirical data is available for research purposes.

Conclusion

While application of analytics in healthcare is possible in all spheres of patient care, it is more about leveraging the power of analytics in rapidly evaluating the true value of techno-medical innovations for human benefits. Analytics makes it possible to make fact-based decisions about adopting and internalizing these latest technological advancements that promises to help us lead a quality life for a few years more.

Jan

It’s New Year again – Happy New Year 2016!

Thanks for your overwhelming response to our insights shared with you over the last year. We are excited to announce the most popular perspectives from 2015 published at Veravizion/Perspectives. These are our biggest stories of 2015 in case you missed them.

One of the wonderful aspects about sharing our insights is appreciating the incredible business acumen, diversity, and depth of thinking of our readers. Our articles, which we call our perspectives, are written after carrying out thorough research on every topic. Our belief is that these articles will push you into thinking about how the (business) world is transforming before our eyes, and how some long-standing business principles may not necessarily hold true today.

As the year is over, take a quick glance at how the world is getting used to being data-driven. Enjoy these stories and let us know about your top content in the comments. In the next one, we will see how the analytics world is likely to unfold in 2016.

This article on Data Science by Veravizion was originally published as the cover story in the July-2015 edition of Computer Society of India – Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July)).“

Historically, leaders of cities, communities, and organizations have been embracing strategic initiatives to ensure long term sustenance and growth of their respective ecosystems. Many a times, these initiatives were ‘intentionally’ directed at bringing about long term transformation of their systems. But do such initiatives specifically aimed at strategic transformation always result in the lasting growth of the entity? We discuss it in this article.

This is the last article in the Digital Business series in which we illustrate how small and medium businesses can transform themselves from mere-physical to also-digital, and be more competitive. We do this by taking a visual example of a fictitious light business of our lovable businessman Bobstick.

We hope you enjoy these stories!

Strategic transformation photo credit: businessinsider

You can also subscribe to our blog –Our Perspectives– to receive interesting articles and insights in email. We would love to read your perspectives and comments on that.

Jul

“This article reposted here was originally published as the cover story in the July-2015 edition of Computer Society of India - Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July))."

Data Science means extraction of knowledge from data. The key word in data science is not data; it is science[1]. Science of something means study of that thing to extract knowledge about it. In most generic sense, the purpose of every data science project is to answer a question (or a set of questions) backed by hard-facts. Academicians and researchers apply scientific principles to get specific answers about a research subject. Similarly, businesses employ data science principles to improve customer engagement, devise growth strategies, optimize operations, and build competitive advantage. This article shares a perspective on what data science really is, how it impacts various industries, what benefits does it offer to organizations – both for-profit and not-for-profit, and what are the key data science trends prevalent today.

DATA SCIENCE: WHAT IT IS (AND ISN’T)

Apparently Peter Naur and John W. Tukey seem to be among the first ones to have treated data analysis within the precincts of science[2]. John W. Tukey, who coined the term ‘bit’, has mentioned it in his 1962 paper ‘The Future of Data Analysis’. In my view, while the term ‘data science’ is relatively young, its application is not. There is an early evidence[3] of 1854, of Dr. John Snow applying scientific principles of data analysis to detect the root cause of The Cholera Epidemic in London. So data science has been around for a while albeit in different forms.

While we tend to associate data science with several other terms such as artificial intelligence, machine learning, data mining, analytics, statistics, computer science, and operations research, each has its own specific meaning that is different from another. Artificial intelligence is intelligence exhibited by machines and it pertains to the creation of a software system that simulates human intelligence. Machine learning is a science that involves development of self-learning algorithms which can be used to make data-driven predictions in a similar but unfamiliar environment. Popular examples include self-driving cars and web searches. Statistics is a study of collection, organization, analysis, and interpretation of numerical information from data. Data mining is the practice of analyzing data using (machine-learning) algorithms and statistical techniques in order to solve a problem. Computer science covers computational complexity, distributed architectures such as Hadoop, data compression, optimization of data flows, and not to mention computer programming languages (such as R, Python, and Perl). Advanced analytics or Analytics is just a marketing driven terminology that applies many of the data science principles to solve complex problems faced by businesses and society. So while the differences are subtle, each one has its own application in industry and academia. Nevertheless, data science overlaps with computer science, statistics, operations research, and business intelligence in many ways and almost completely encompasses data mining and machine learning.

The subtle differences notwithstanding, data science is an independent discipline which amalgamates statistics, computing skills, and domain knowledge. At the core, data science helps in deriving valuable insights from data. The data science process involves data collection, data pre-processing and cleaning, data modelling and analysis, and insights generation which are applied within a given functional domain to make decisions. Although the process is similar to knowledge discovery and data mining (KDD), a data scientist requires computing skills and domain knowhow to arrive at context-specific decisions. The person working in data science needs to exhibit three distinct skills applied in the different phases of a data science project. As shown in EXHIBIT-A[4], an individual with data science expertise possesses (or needs to possess) a combination of mathematics and statistics knowledge, hacking skills, and substantial domain understanding. The hacking skills include familiarity (but not necessarily proficiency) with software programming but more importantly, a propensity at being able to manipulate any type of data. This is because real-world data hardly exists in a nice tabular format. It[5] is scattered in thousands of text files or on hundreds of web sites or in numerous unstructured excel sheets at best. True data scientists that possess all the three skills are not abundant; because the role entails making sense of amorphous data, deriving bespoke models, and developing algorithms to analyse a complex problem specific within a domain.

Unfortunately, simply churning out numbers or fiddling with inefficient models rarely solves a problem. This is the reason data scientist is one of the most coveted roles in industry today.

Data science is being applied in many industries. Some of the uses in various industries include weather forecasting, intuitive search in online search technology, customer engagement in retail and consumer products and services, fraud detection in banking and credit cards, prediction of sources of energy in Oil and Gas, evidence based medicines in healthcare, and sentiment analysis from social network feeds. Some fields that are routinely implementing analytics services are eCommerce, retail, consumer products and services, financial services, insurance, pharmaceuticals, manufacturing, telecommunications, and high-tech.

HUNTING PEARLY INSIGHTS IN THE OCEAN OF DATA WITH DATA SCIENCE

More and more businesses are embracing data science and analytics in multiple organizational functions. There are mainly three most common ways in which data science is deployed depending on the size of an organization. Large corporations usually deploy their own in-house analytics departments by recruiting data analysts. Business leaders in large corporations typically have humongous quantities of data to sift through in order to make decisions that are important for their business growth. While having an in-house analytics team may not always be an ideal way for institutionalizing data science, even for large corporations, they seem to be driven by large amount of resources at their disposal. Secondly, some companies prefer to buy a COTS (Commercial-Off-The-Shelf) product to cater to some standard requirement. Thirdly, many mid-to-large sized companies prefer to employ customized data science or analytics services to solve their specific data analysis and business operational requirement. This option seems ideal for businesses looking for the flexibility to hire precise services for their bespoke needs.

While the data science projects in most for-profit organizations are getting more and more complex, the fundamental purpose underlying these projects remain the same – to achieve sustainable growth and improve profitability for their businesses. To that effect, the companies put data science into action to gain meaningful insights into their customers, operational processes, supply chain and logistics, product and/or service usage, financial aspects, and future business performance. Conventionally, data science has mostly been applied for market research and market segmentation. However, businesses have a lot more at stake with every business decision as competition has become more and more intense. Gone are the days when business decisions used to be taken on gut-feeling. In today’s globalized world, every major business decision needs to be data-driven. Data science assists organizations and individuals in making fact-based decisions that they can take and defend confidently. That is why it has become essential for organizations, business or otherwise, to deploy data science projects in every division responsible for making any kind of decisions. Some of the types of data science and analytics projects include customer focused analytics through clustering, recommendation engines, root cause analysis, automated rule engines, conjoint analysis to quantify perceived value of features offered, process simulations for operational analysis, predictive modeling for business forecasting, and clustering analysis to identify anomalies, just to name a few.

BENEFITS FROM IMPLEMENTING DATA SCIENCE INITIATIVES

There are some fantastic examples of business organizations gaining huge benefits by systematically and strategically deploying analytics initiatives that involve data science and ethnographic research. Procter & Gamble has institutionalized the data and design thinking approach to such as extent that it is now ingrained into their DNA. The result is that P&G boasts of more than 20 billion-dollar brands in their product kitty. Amazon, a technology company and not just an eRetailer, is really surviving and thriving by understanding customer preferences through the implementation of numerous algorithms. It has helped them to grow quickly from selling just books online in 1996 to target-selling twenty million products in countless other categories. There are many examples of smaller companies that streamlined their processes and implemented analytics based strategies to grow and enter into the big league. Data science initiatives within companies have rendered meaningful insights to drive their firm’s customer experience. These companies have utilized the insights to define their business growth strategies and pursue a culture of data-driven decision making. The benefits include getting pointers to new growth areas, generating ideas to introduce innovative new products, decreasing cost bases and improving productivity to boost profitability, identifying risks of obsolete technologies in their processes, detecting bottlenecks in supply chain, and streamlining inefficient operations.

Even as data science is rapidly changing the business world, it is also spreading its influence on other sectors such as academic research, governments, and social organizations. While the data deluge has increased the complexity for these sectors to analyze the data in a timely manner, it has also opened a plethora of opportunities for them.

Academic institutions in regions such as US, UK, and some countries in Asia are facing sustainability issues due to severe cuts in funding and grants. They are able to apply data science within their own institutional spheres to identify their respective competitive advantage and attract the right students to strengthen their reputation further. Similarly, medical research institutions are now able to work on projects like genome research, DNA sequencing, and stem-cell research for treatment of fatal diseases such as cancer and AIDS. Economists are able to analyze the publicly available data to determine relationships between income levels, education, health, and quality of life.

Governments and public sector organizations are concerned about issues such as monitoring and prevention of terrorist activities, early-detection and control of pandemics, and uniform aid distribution among the poorer countries, which they are able to tackle by sponsoring appropriate data science initiatives.

TACKLING CHALLENGES ALONG THE WAY

Data privacy and security concern has been one of the main reasons keeping some businesses from adopting data science. Moreover companies are facing real challenges in terms of bad quality of data, data inconsistencies, unreliable third party data, and information security. Nonetheless, all roads to meaningful business insights lead through data, whether it is organizational or public. Businesses need to put in place appropriate mechanisms to share data in a controlled manner with analysts and service providers in order to generate hidden insights that can be utilized for business benefits. Data breaches and data thefts remain a valid concern too. Past incidents, albeit few and sporadic, of customer confidential information getting stolen have deterred some from initiating analytics projects. However, business organizations are coming around to the fact that they are fast losing their competitive advantage to rivals due to staying away from analytics. Increasing number of organizations is taking up analytics to secure and grow their businesses as they do not want to be left behind any more. Organizations will increasingly recognize that it is not possible to operate in a 100 percent secured environment. Once organizations acknowledge that, they can begin to apply more-sophisticated risk assessment and mitigation tools. They will look to embed security at multiple levels viz. application-level, execution-level, storage-level, and even contract level. Interestingly, analytics itself is proving to be a great mechanism for security breach prevention.

KEY TRENDS AND THE ROAD AHEAD

In some of the western countries, data science has been thoroughly internalized within large corporations. Even the smaller businesses there employ analytics services to achieve specific business objectives. In India, while the (few) big corporations seem to be deploying such initiatives, most other organizations are still in the nascent stage. One survey of SME business owners cited that most common reasons for the slow pace of embracing [data science] are lack of awareness about the value offered by analytics, dearth of skilled resources, apprehension about technological complexity, cost and ROI concerns, and data security risks.

Notwithstanding the current adoption level, businesses are realizing that they may be taking a big risk not considering data science and analytics as a potent competitive strategy. There is a tremendous rise of personal data originating from social-media, sensor-originated data from wearables, and the Internet of Things (IoT) with the recent surge in the use of smartphones. More and more human actions are generating Exabytes of data today. To get a sense of the amount of data being generated, let’s just say that we will need around 50 billion trees made into paper to print 1 Exabyte of data. That’s roughly 9 huge stacks of papers, each touching Mars from Earth. This enormous amount of data will be of no use if not analyzed and utilized appropriately.

These trends are pushing businesses to re-think their business and growth strategies. There is an increased focus on teaching data science based courses by colleges and universities worldwide. Companies are realizing that the business environment has become uncertain with the fast pace of technological and demographical changes. As a result, many organizations are allocating higher budgets for deploying customized analytics for their businesses to deepen customer understanding, engage customers through multiple channels, identify new sources of revenue, improve productivity and profitability, streamline business processes, and build competitive advantage. Going forward, use of customized analytics will become pervasive. More and more organizations will develop their unique value propositions around the valuable insights they gain about their existing and prospective customers.

Implementing data science initiatives to build competitive advantage is a matter of leading and not following the pack. In an industry competing for the finite market share, early-adopters of data science best practices will be the eventual winners.

“This article reposted here was originally published as the cover story in the July-2015 edition of Computer Society of India – Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July)).”

Jun

Thanks for your overwhelming response to the ‘achieve strategic transformation for enduring growth – part-I‘ of this two-part series. In the part-I of this article, we agreed with Mark that strategic transformation is indeed the key to enduring growth. However, we differed on the way to achieve transformation.

So how do you achieve strategic transformation for enduring growth of your business?

In part-I, we asserted that the secret to achieving strategic transformation is to be fiercely customer-centric.

In this part, let us discuss how you can transform your business and grow it further keeping customer-centricity at the core of your business strategy.

When you become passionately customer-centric, you make your customers’ delight the overarching goal of your organization. ‘Customer delight’ becomes the Shinbashira – the central pillar – on which your organization stands. You listen to your customers and place your customers (and consumers) at the centre of everything you offer. You direct your resources toward satisfying (and exceeding) the needs and wants of your consumers. As their needs change, the solution you offer changes, and the business strategy behind delivering those solutions changes too. You become so agile in responding to your customer’s changing tastes that you barely notice the incremental changes taking place in your organization. This continuous change becomes second nature to your organization.

And when you look back over a period of time, you realize that you have moved so significantly from your original business situation that there is hardly anything common between the old face and the new face of your organization. It is like the organization undergoing morphing.

This is when you have achieved transformation, without consciously embarking on doing that.

You don’t TAKE disruptive actions for strategic transformation; you take well-conceived actions with specific goals that BRING ABOUT ‘disruptive transformation’. There is a difference!

But this is a very simplified view of an organization going through transformation.

What actually happens (or has to happen) on ground is far more complex and far too deliberate.

Let us see how.

Our analysis of the top-100 brands revealed that when organizations commit themselves to becoming customer-centric, they tend to take these five steps so as to focus on creating maximum value for their customers:

They meticulously track usage of their products and services to figure out their customer’s usage patterns. This may involve analysing usage data from customers and in some cases their customer’s customers, who are using products that incorporate your product.

They strive to understand their customers thoroughly to comprehend the real reasons behind their customers’ purchases and apply that knowhow to identify real profitable customers. They perform detailed analysis of the customers’ data to generate deep insights into their purchasing behaviour and to spot any changes in them.

They incorporate their customers’ feedback into R&D to continually improve their products and services in line with the changing consumer expectations. Once in a while, this may result in conceptualizing an innovative product not yet in the market.

They put in place mechanisms to refine their operational strategies and organizational processes to address the changing priorities.

Most importantly, they use the feedback to calibrate their future roadmap and formulate the long-term growth strategy for their business.

These actions, when deployed effectively, enable these companies to embrace one or more of the three growth strategies: Market Expansion, Product/Industry Expansion, and Operational Improvement.

Let us briefly look at each of them.

1. MARKET EXPANSION – Business growth by entering into new market(s):

When you are customer-centric, you strive to know the face of your most profitable customers and the benefits for which they are buying from you. Your growth objective is to identify more of such customers globally who may benefit from your offering. This insight assists you to explore new customer bases in newer markets. The way this objective is achieved varies from company to company. Some companies merge with or acquire other businesses having presence in the target market, and leverage that presence to market their products. Others may form joint-ventures on the agreement to share technology and markets. Yet others may plan to grow organically by entering a new market on their own.

Coca-Cola, MTV, Starbucks, Nestlé, Heineken, and scores of other companies have successfully located their target customer segments across the world. They achieved international expansion over an extended period of time transforming their businesses from being local entities to becoming global market-leaders.

As your customers’ preferences change, the products or services they employ to do their job (need to) change too. When you are constantly tracking this change, you get innovative ideas for improving your existing product lines and introducing new ones. A good grasp on customers’ needs in new areas can propel you to launch new product categories that didn’t exist previously.

Procter & Gamble has developed this expertise in consumer research through ethnographic methods; they claim to co-create new products or new line of existing products by closely observing how their consumers use the existing products.

Amazon is an excellent example of growth through product or category expansion. Pursuing the vision of becoming ‘earth’s most customer-centric company’, Amazon is single-mindedly striving to make customer’s (online) shopping experience easy, comfortable, and pleasant. In fact, they are in the process of disrupting the entire book publishing industry by planning to publish and sell every future book online. The established book-publishers lost touch with the changing consumer need of ‘reading books anytime-anywhere without having to spend time buying it physically’, and now their survival itself is threatened by the digital publishing industry; whereas the likes of Amazon, with relentless focus on the consumers, are thriving.

This strategy involves creating differentiation in your business model and/or operations. Companies are leveraging their organizational data by applying the latest analytics techniques to mine insights. These insights are utilized to devise new distribution channels, optimize logistics and supply chain management, improve operations by deploying superior technology, and streamline existing processes. Relentlessly pursuing these strategies eventually renders them distinctive advantage over their competition, and helps them grow in the marketplace.

Michael Dell grew his company by sensing an opportunity among PC-savvy consumers who enjoyed the convenience of customizing their PC and buying it online to be delivered in days. Thus Dell has developed an innovative business model of ordering PC online which enabled them to lower their PC prices to impossibly low levels.

These strategies have the potential to propel your organization into a bigger growth curve.

And the key to doing this is to understand your customers as thoroughly as you can!

In this world of disruptive innovations, business owners and CEOs must focus all their efforts in deeply understanding their customers. When you become ardently customer-centric, you set yourself up for strategic transformation that is key to enduring growth of your business.

You can also subscribe to our blog –Our Perspectives– to receive interesting articles and tips in email. We would love to read your perspectives and comments on that.

May

In the April edition of Stanford Business Insights, Mark Leslie states that successful enterprises have a ‘Cycle of Life’ which lives through five phases: Product or Service Development during Start-up, Market Entry, Growth, Maturity, and Decline. He says that during the decline phase, revenue slows down and flattens out, margins stabilize at lower levels, operational expenses rise to unsustainable heights, and company spirals into negative growth marked by layoffs, high burn rates, and eventual bankruptcy or liquidation. Mark says that the key to enduring growth, to manage this eventuality, is strategic transformation.

We agree!

When companies are growing for decades, deliberately or not, they tend to undergo some form of transformation, irrespective of how they are growing.

Mark further adds that there’s a moment along the corporate Arc of Life between growth and maturity – a point he calls ‘the sweet spot for optionality’ – when companies should take initiative to steer into uncharted waters (a new line of products, a new business category, a new industry?). In other words, opportunity-driven leaders should attempt to transform the business rather than risk growth by continuing along the well-marked path through operational excellence.

On these two points, we do not agree!

We do not concur that continuing along the path of operational excellence does not fetch growth.

We are not of the opinion that the way to strategic transformation is to just change your path to uncharted waters.

Let us explain why:

Regarding the first point, let us hypothesize for a moment that operational excellence does not fetch long term growth. Then how do we explain the growths of the likes of GM and Wal-Mart in the past? After-all Alfred P. Sloan, Jr. and Sam Walton are known to have led their companies on the back of operational improvements.

Moreover, prominent brands like Toyota, McDonald’s, Zara, and Subway have grown primarily through laser-sharp focus on operational efficiencies. Toyota has continually enhanced its assembly-line operations leveraging its legendary Toyota Production System. McDonald’s has continued serving its fast foods with the promise of ‘fast and convenient’ service. Zara has reduced its shopfloor-to-store cycle time of its fashion garments to just five weeks compared to industry average of six months.

We thought that studying the growth strategies of the top-100 brands, which have been growing for decades, would point to the key behind enduring growth of businesses. It will help us prove or disprove the hypothesis.

A quick analysis, as shown in EXHIBIT-A, revealed that more than two-third of the top-100 brands achieved enduring growth through strategic transformation. Interestingly, almost one-third of the top-brands have attained steady growth primarily through operational improvements.

EXHIBIT-B illustrates that there is no obvious correlation between a company’s lifespan and the revenues it earns, with respect to their growth strategies. However, it also establishes that transformation-driven companies seem to out-live and out-earn the operationally-driven companies, especially over the longer term.

So the above two analyses disprove the hypothesis that companies cannot grow with operational excellence; and it also buttresses the theory that strategic transformation is the key to enduring growth.

The second point in a way suggests that business leaders see the sweet spot of optionality as an opportunity to hurtle their company to the next level by changing their course into uncharted waters. But it sounds a bit ambiguous and fuzzy in terms of identifying the sweet spot of optionality almost to the point of being too simplistic.

This is because when you DO transformations for the SAKE of transformation, then you achieve just that – a transformation, without any real benefits from it.

You cannot just APPLY transformation in business as easily as you apply a Fourier or a Laplace transform in engineering mathematics. (Bad analogy I know, but you get the point).

Strategic transformation is a result, and not a set of random actions.

Transformation is an effect, and not a goal.

Transformation HAPPENS over time when you take well-conceived actions, persistently, with single-minded focus on a specific goal.

In our analysis, surprisingly (or unsurprisingly) there was one common theme (or goal) observed across all these 100 leading brands. That theme is Customer-Centricity. Amazon wants to be Earth’s most customer-centric company. Apple demonstrates its customer-centricity in the intuitive products it designs. Gillette develops a different type of razor for each customer segment that has a different shaving habit. Oracle invests strategically into technology that customers are likely to embrace in the future, as it did with its E-Business Suite. There are as many examples of customer centricity as the market-leaders around.

In sum, the point is this: The secret to achieving strategic transformation for enduring growth of your company is to be fiercely customer-centric. When you are passionately customer-focussed in your business, the customer will show you the way to achieving strategic transformation.

In part-II, we will discuss how exactly do you transform your business with customer-centricity as your main strategy.

(Cover photo credit: theatlantic)

You can also subscribe to our blog –Our Perspectives– to receive interesting articles and tips in email. We would love to read your perspectives and comments on that.